snapconfig 0.6.0

Superfast config loader for Python, powered by Rust + rkyv

Superfast config loader for Python, powered by Rust + rkyv.
See benchmarks below.

What it does

• Parses JSON / YAML / TOML / INI / .env, compiles once, then memory‑maps the cache
• Zero-copy reads via Rust rkyv + mmap, so repeated loads stay fast and page-shared across processes
• Dict-like access in Python ([], .get, in, len, iteration) plus dot-notation lookup
• Cache freshness check on load; caches are written atomically to avoid torn files

Inspiration

• Need for superfast loading of large JSON files and other configs across processes/workers in AI workflow orchestrators.
• uv package manager, which uses rkyv to deserialize cached data without copying.

Installation

pip install snapconfig

Quick start

import snapconfig

# Load any config format, automatically cached on first load
config = snapconfig.load("config.json")
config = snapconfig.load("config.yaml")
config = snapconfig.load("pyproject.toml")
config = snapconfig.load("settings.ini")

# Access values like a dict
db_host = config["database"]["host"]
db_port = config.get("database.port")  # dot notation supported

# Load .env files
env = snapconfig.load_env(".env")
snapconfig.load_dotenv(".env")  # populates os.environ

How it works

First load:    config.json → parse → compile → config.json.snapconfig (cached)
                                                    ↓
Subsequent:                              mmap() → zero-copy access (~30µs)

1. First load: Parses source file and compiles to optimized binary cache
2. Subsequent loads: Memory-maps the cache file for instant zero-copy access

The cache file is automatically regenerated when the source file changes.

Benchmarks (local run)

Numbers from running pipenv run python benchmark.py on an M3 Pro (see benchmark.py for exact scenarios).

Takeaways:

• Cached reads stay in the low milliseconds down to tens of microseconds; big files benefit most.
• Cold loads beat YAML/ENV/TOML parsers; Python’s json still wins cold, but cached loads dominate.

When snapconfig shines

• CLI tools that start frequently
• Serverless functions with cold starts
• Multiple worker processes reading the same config
• Large config files (package-lock.json, monorepo configs)

Cold vs cached

Scenario	What Happens	vs JSON	vs YAML/TOML/ENV
Cold (first load)	Parse + compile + write cache	Slower	3-170x faster
Cached (subsequent)	mmap() only	3-5,000x faster	50-7,000x faster

Cold loads are slower than Python's json module (it's highly optimized C code), but faster than pyyaml, tomllib, and python-dotenv. The real payoff comes on cached loads.

Supported formats

Format	Extensions	Parser
JSON	.json	simd-json
YAML	.yaml, .yml	serde_yaml
TOML	.toml	toml
INI	.ini, .cfg, .conf	rust-ini
dotenv	.env, .env.*	custom

API Reference

Loading

# Load with automatic caching (recommended)
config = snapconfig.load("config.json")
config = snapconfig.load("config.json", cache_path="custom.snapconfig")
config = snapconfig.load("config.json", force_recompile=True)

# Load directly from cache (skips freshness check)
config = snapconfig.load_compiled("config.json.snapconfig")

# Parse string content (no caching)
config = snapconfig.loads('{"key": "value"}', format="json")
config = snapconfig.loads("key: value", format="yaml")

dotenv support

# Load .env with caching
env = snapconfig.load_env(".env")
env = snapconfig.load_env(".env.production")

# Load into os.environ
count = snapconfig.load_dotenv(".env")
count = snapconfig.load_dotenv(".env", override_existing=True)

# Parse .env string
env = snapconfig.parse_env("KEY=value\nDEBUG=true")

Cache management

# Pre-compile config (e.g., during Docker build)
snapconfig.compile("config.json")
snapconfig.compile("config.json", "config.snapconfig")

# Check cache status
info = snapconfig.cache_info("config.json")
# {'source_exists': True, 'cache_exists': True, 'cache_fresh': True, ...}

# Clear cache
snapconfig.clear_cache("config.json")

SnapConfig object

config = snapconfig.load("config.json")

# Dict-like access
config["database"]["host"]
config["database"]["port"]

# Dot notation for nested access
config.get("database.host")
config.get("database.port", default=5432)

# Iteration
for key in config:
    print(key, config[key])

# Membership
"database" in config  # True

# Info
len(config)           # Number of keys
config.keys()         # List of keys
config.to_dict()      # Convert to Python dict
config.root_type()    # "object", "array", etc.

Cross-process benefits

When multiple processes load the same cached config:

# Worker 1, Worker 2, Worker 3...
config = snapconfig.load("config.json")  # All share same memory pages

The operating system's virtual memory system ensures all processes share the same physical memory pages via mmap(). This is particularly useful for:

• Prefect/Celery workers
• Gunicorn/uWSGI workers
• Multiprocessing pools
• Serverless function instances

Pre-compilation

For production deployments, pre-compile configs during build:

# Dockerfile
RUN python -c "import snapconfig; snapconfig.compile('config.json')"

# CI/CD
- run: python -c "import snapconfig; snapconfig.compile('config.json')"

This ensures the first load in production is already cached.

Acknowledgements

snapconfig is built on:

• rkyv - Zero-copy deserialization framework for Rust
• PyO3 - Rust bindings for Python
• simd-json - SIMD-accelerated JSON parser
• maturin - Build and publish Rust Python extensions

License

MIT